A Standardized Reference Data Set for Vertebrate Taxon Name Resolution

Abstract

Taxonomic names associated with digitized biocollections labels have flooded into repositories such as GBIF, iDigBio and VertNet. The names on these labels are often misspelled, out of date, or present other problems, as they were often captured only once during accessioning of specimens, or have a history of label changes without clear provenance. Before records are reliably usable in research, it is critical that these issues be addressed. However, still missing is an assessment of the scope of the problem, the effort needed to solve it, and a way to improve effectiveness of tools developed to aid the process. We present a carefully human-vetted analysis of 1000 verbatim scientific names taken at random from those published via the data aggregator VertNet, providing the first rigorously reviewed, reference validation data set. In addition to characterizing formatting problems, human vetting focused on detecting misspelling, synonymy, and the incorrect use of Darwin Core. Our results reveal a sobering view of the challenge ahead, as less than 47% of name strings were found to be currently valid. More optimistically, nearly 97% of name combinations could be resolved to a currently valid name, suggesting that computer-aided approaches may provide feasible means to improve digitized content. Finally, we associated names back to biocollections records and fit logistic models to test potential drivers of issues. A set of candidate variables (geographic region, year collected, higher-level clade, and the institutional digitally accessible data volume) and their 2-way interactions all predict the probability of records having taxon name issues, based on model selection approaches. We strongly encourage further experiments to use this reference data set as a means to compare automated or computer-aided taxon name tools for their ability to resolve and improve the existing wealth of legacy data.

Taxonomic perils and pitfalls of dataset assembly in ecology: a case study of the naturalized Asteraceae in Australia

“…Our cleaning protocol removed 27% (88 out of 328 taxon names at step 2) based on similar nomenclatural issues. Although this percentage is not as high as that reported in Zermoglio et al (2016), it still represents the highest number of taxa requiring attention (excluding the removal of duplicate names). The high prevalence of synonymy is not surprising as this type of issue is the most difficult and time consuming to solve (Zermoglio et al 2016).…”

“…Although this percentage is not as high as that reported in Zermoglio et al (2016), it still represents the highest number of taxa requiring attention (excluding the removal of duplicate names). The high prevalence of synonymy is not surprising as this type of issue is the most difficult and time consuming to solve (Zermoglio et al 2016). In this context, consultation with specialist taxonomists is highly desired (Gotelli 2004).…”

“…This difficulty, compounded by the global decline of taxonomic expertise, leads to situations where ecological datasets are often used without much attention being given to the quality of the underlying taxonomic data (Maldonado et al 2015). This is concerning because a lack of appropriate taxonomic consideration can have serious impacts on the robustness of outcomes from large dataset studies (Jansen and Dengler 2010, Duarte et al 2014, Zermoglio et al 2016). The protocol we have presented here is helpful because it brings together an integrated management plan that combines usually disparate elements of dataset assembly which are not always considered together in a systematic way for plant ecological datasets.…”

“…In addition, it is critical to understand that automated approaches are still reliant on the sources used for nomenclatural cleaning being regularly maintained and updated to reflect current taxonomic knowledge. Unfortunately, the approach to ensuring currently valid names are used has generally been haphazard in broader curatorial practice (Costello and Wieczorek 2013), but there is much scope for it to become more systematic as these datasets grow and receive more attention based on their value in the age of Big Data (Zermoglio et al 2016).…”

“…A small number of studies, however, have begun to empirically address the issue of taxonomic reliability in the sorts of large datasets available for use in large dataset studies in animal ecology. Zermoglio et al (2016), for example, analysed 1000 scientific names taken at random from VertNet, an aggregator of vertebrate biodiversity data from natural history collections. They found that less than 47% of names were currently valid.…”